Forecasting urban traffic flow by SVR with continuous ACO

Hong, Wei‐Chiang; Dong, Yucheng; Zheng, Feifeng; Lai, Chien-Yuan

doi:10.1016/j.apm.2010.09.005

Cited by 147 publications

(62 citation statements)

References 45 publications

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“…A support vector regression model was also applied for short-term traffic flow prediction in combination with the ant colony optimisation algorithm [25]. The approach has been tested on data from Taiwan.…”

Section: State Of the Artmentioning

confidence: 99%

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Prediction of Traffic Intensity for Dynamic Street Lighting

Bielecka

Bielecki

Ernst

et al. 2017

Annals of Computer Science and Information Systems

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Abstract-In this paper, the problem of short-term prediction of traffic flow in a city traffic network is considered. This prediction is performed in order to provide input data to a dynamic control system for street lighting. The forecasting is done by a multi-layer using artificial neural network. Because of the limited number of sensors, the data is insufficient to describe the relation between the traffic intensity at a given point and the points in which the flow intensity is measured. The proposed approach is tested by using data from the centre of Kraków. The prediction error turned to be low.

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Section: State Of the Artmentioning

confidence: 99%

“…Traffic flow prediction is a problem which has been intensively studied for a long time [20], [21], [22], [23], [24], [25], [26], [27], [28], [29], [30], [31], [32], [33]. Both the traffic on highways and the traffic in city networks is considered.…”

Section: State Of the Artmentioning

confidence: 99%

Prediction of Traffic Intensity for Dynamic Street Lighting

Bielecka

Bielecki

Ernst

et al. 2017

Annals of Computer Science and Information Systems

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“…Nonetheless, a time series may still exhibit non-stationary behaviour because of the presence of a trend or seasonal variations. To address this problem Box and Jenkins (1970) suggested the use of a SARIMA (p, d, q) × (P, D, Q)S model, which can be formulated as follows (Box & Jenkins 1970;Hong et al 2011): This article will make use of the AIC to compare the goodness of fit of the different model specifications. Also, the adequacy of each model will be verified by the Ljung-Box test (Ljung & Box 1978) and the autocorrelation function (ACF) of the standardised residuals.…”

Section: The Seasonally Adjusted Autoregressive Integrated Moving Avementioning

confidence: 99%

Complementing South African inflation surveys: A suitable forecasting tool

Heerden

Heymans

Seetharam

2018

J. econ. financ. sci.

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Central banks currently perform inflation expectation surveys in order to better align their inflation expectations with that of the general public. However, surveys are time-consuming, complicated, expensive and not always accurate, thus compromising the credibility of these expectations. The complexity of inflation targeting and the difficulty of forecasting in real time can also cause policymakers to consider more basic models, which can lead to inexact forecasts. This article employs less complicated models, such as the seasonally adjusted autoregressive integrated moving average and Holt-Winters exponential smoothing models, to provide equally reliable forecasts. A more complex approach in the form of a non-linear autoregressive neural network process was also employed to model the strategic and rational manner in which the general public formulates their expectations. Overall, the forecast estimates provided by these models were superior when compared with the inflation expectations provided by the International Monetary Fund, South African Reserve Bank and Bureau for Economic Research.

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“…New methods and techniques for improving the prediction accuracy are continuously presented [1,2]. Currently, many methods such as the regression method [3,4], time-series analysis [5,6], Kalman filter [7], grey model [8,9], spectral analysis [10,11], chaos theory [12], time-space model [13,14], neural network [15] and SVM [16,17] are widely used in traffic flow prediction.…”

Section: Introductionmentioning

confidence: 99%

Grey Coupled Prediction Model for Traffic Flow with Panel Data Characteristics

Yang

Xiao

Mao

et al. 2016

Entropy

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This paper studies the grey coupled prediction problem of traffic data with panel data characteristics. Traffic flow data collected continuously at the same site typically has panel data characteristics. The longitudinal data (daily flow) is time-series data, which show an obvious intra-day trend and can be predicted using the autoregressive integrated moving average (ARIMA) model. The cross-sectional data is composed of observations at the same time intervals on different days and shows weekly seasonality and limited data characteristics; this data can be predicted using the rolling seasonal grey model (RSDGM (1,1)). The length of the rolling sequence is determined using matrix perturbation analysis. Then, a coupled model is established based on the ARIMA and RSDGM(1,1) models; the coupled prediction is achieved at the intersection of the time-series data and cross-sectional data, and the weights are determined using grey relational analysis. Finally, numerical experiments on 16 groups of cross-sectional data show that the RSDGM(1,1) model has good adaptability and stability and can effectively predict changes in traffic flow. The performance of the coupled model is also better than that of the benchmark model, the coupled model with equal weights and the Bayesian combination model.

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Forecasting urban traffic flow by SVR with continuous ACO

Cited by 147 publications

References 45 publications

Prediction of Traffic Intensity for Dynamic Street Lighting

Prediction of Traffic Intensity for Dynamic Street Lighting

Complementing South African inflation surveys: A suitable forecasting tool

Grey Coupled Prediction Model for Traffic Flow with Panel Data Characteristics

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